• Journal of Biosystems Engineering
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• v.42 no.2
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• pp.75-79
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• 2017

The use of tractors to carry out agricultural work has played an important role in mechanizing the agricultural sector. A repairable mechanical system (such as an agricultural tractor) is subject to deterioration or failure. In this study, a regression model was used to predict the failure rate of a John Deere 3140 tractor. The machine failure pattern was carefully studied, and key factors affecting the failure rate were identified in five regions of the Khouzestan province. Through a questionnaire, data was obtained from farm records. This data was grouped into six sub-groups, according to the annual use hours (AUH) and the manner in which the tractors were stored. Results showed that AUH and storage policies affected failure rate slightly. With an increase in the age of the tractors, the failure rate in the tractors used for 1050-2000 hours annually and stored outdoors was higher than those used for 200-1000 hours annually and stored in sheds. When the tractors were of the same age, the slope of the curve in the 200-1000 annual use hours increased gradually and then rapidly, but failure rate in the 1050-2000 annual use hours was high from the beginning, and subsequent increase in this value was almost uniform. As a result, it can be said that with an increase in the annual use hours, the failure and breakdown rate in John Deere 3140 tractors rapidly increases, but maintenance conditions only slightly affect the failure and breakdown rate.

• Journal of Applied Reliability
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• v.17 no.4
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• pp.296-304
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• 2017

Purpose: Recent weapon systems in defense have increased the complexity and importance of software when developing multifunctional equipment. In this study, we analyze the accuracy of the proposed software reliability model when applied to weapon systems. Methods: Determine the similarity between software reliability analysis results (prediction/estimation) utilizing data from developing weapon systems and system failures data during operation of weapon systems. Results: In case of a software reliability prediction model, the predicted failure rate was higher than the actual failure rate, and the estimation model was consistent with actual failure history data. Conclusion: The software prediction model needs to adjust the variables that are appropriate for the domestic weapon system environment. As the reliability of software is increasingly important in the defense industry, continuous efforts are needed to ensure accurate reliability analysis in the development of weapon systems.

• Journal of Applied Reliability
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• v.9 no.2
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• pp.121-130
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• 2009

The power law process model the Rate of occurrence of failures(ROCOF) with monotonic trend during the operating time. However, the power law process is inappropriate when a non-monotonic trend in the failure data is observed. In this paper we deals with the reliability modeling of the failure process of large and complex repairable system whose rate of occurrence of failures shows the non-monotonic trend. We suggest a sectional model and a change-point test based on the Schwarz information criterion(SIC) to describe the non-monotonic trend. Maximum likelihood is also suggested to estimate parameters of sectional model. The suggested methods are applied to field data from an repairable system.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.113-122
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• 2008

In this paper, we consider a periodic preventive maintenance policy in which each preventive maintenance reduces the hazard rate of amount proportional to the failure intensity, which increases since the system started to operate. And the effect of preventive maintenance at each preventive maintenance epoch is different. The expected cost rate per unit time for the proposed model is obtained. We discuss the optimal number N of the periodic preventive maintenance and the optimal period x, which minimize the expected cost rate per unit time and obtain the optimal preventive maintenance schedule for given cost structures of the model. A numerical example is given for the purpose of illustrating our results when the failure time distribution is Weibull distribution.

• Structural Engineering and Mechanics
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• v.38 no.4
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• pp.479-501
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• 2011

Even though fiber reinforced polymer (FRP) has been widely used as a retrofitting material, the FRP behavior and effect in FRP retrofitted structure under blast loading, impulsive loading with instantaneous time duration, has not been accurately examined. The past studies have focused on the performance of FRP retrofitted structures by making simplifications in modeling, without incorporating accurate failure mechanisms of FRP. Therefore, it is critical to establish an analytical model that can properly consider the specific features of FRP material in evaluating the response of retrofitted concrete structures under blast loading. In this study, debonding failure analysis technique for FRP retrofitted concrete structure under blast loading is suggested by considering FRP material characteristics and debonding failure mechanisms as well as rate dependent failure mechanism based on a blast resisting design concept. In addition, blast simulation of FRP retrofitted RC panel is performed to validate the proposed model and analysis method. For validation of the proposed model and analysis method, the reported experimental results are compared with the debonding failure analysis results. From the comparative verification, it is confirmed that the proposed analytical model considering debonding failure of FRP is able to reasonably predict the behavior of FRP retrofitted concrete panel under blast loading.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.233-238
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• 2001

This paper deals with demand forecasting of parts in an automobile model which has been extinct. It is important to estimate how much inventory of each part in the extinct model should be stocked because production lines of some parts may be replaced by new ones although there is still demands for the model. Furthermore, in some countries, there is a strong regulation that the automobile manufacturing company should provide customers with auto parts for several years whenever they are requested. The major characteristic of automobile parts demand forecasting is that there exists a close correlation between the number of running cars and the demand of each part. In this sense, the total demand of each part in a year is determined by two factors, the total number of running cars in that year and the failure rate of the part. The total number of running cars in year k can be estimated sequentially by the amount of shipped cars and proportion of discarded cars in years 1, 2,$\cdots$, i. However, it is very difficult to estimate the failure rate of each part because available inter-failure time data is not complete. The failure rate is, therefore, determined so as to minimize the mean squared error between the estimated demand and the observed demand of a part in years 1, 2,$\cdots$, i. In this paper, data obtained from a Korean automobile manufacturing company are used to illustrate our model.

• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.13-19
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• 2015

Railway vehicle equipment goes back again to the state just before when failure by the repair. In repairable system, we are interested in the failure interval. As such, a statistical model of the point process, NHPP power law is often used for the reliability analysis of a repairable system. In order to derive a quantitative reliability value of repairable system, we analyze the failure data of the air brake system of the train line 7. The quantitative value is the failure intensity function that was modified, converted into a cost-rate function. Finally we studied the optimal number and optimal interval in which the costs to a minimum consumption point as cost-rate function. The minimum cost point was 194,613 (won/day) during the total life cycle of the braking system, then the optimal interval were 2,251days and the number of optimal preventive maintenance were 7 times. Additionally, we were compared to the cost of a currently fixed interval(4Y) and the optimum interval then the optimal interval is 3,853(won/day) consuming smaller. In addition, judging from the total life, "fixed interval" is smaller than 1,157 days as "optimal interval".

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.791-798
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• 2014

Wu and Clements-Croome (2005) suggest the preventive maintenance (PM) model with random maintenance quality. They assume that each PM resets the failure rate to zero and the rate of increases of the failure rate gets higher after each additional PM. However a system may not be restored to as good as new immediately after the completion of PM. Thus, this paper modifies the Wu and Clements-Croome's PM model and then the optimal PM policy is suggested. To determine the optimal PM policy, we utilize the expected cost rate per unit time for our model. That is, we obtain the optimal number and the optimal period by minimizing the expected cost rate per unit time. The numerical examples are presented for illustrative purpose.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.723-731
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• 1998

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and hazard rate function based on samples from a Burr type X failure model. Bayes calculations can be implemented by means of the Gibbs sampler and a numerical study us provided.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.152-162
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• 1999

In this paper we develope a new nonparametric estimator of the reliability in strength-stress model. This estimator is constructed using the maximum likelihood estimate of cumulative failure rate in the class of distributions which have piecewise linear failure rate functions between each pair of observations. Large sample properties of our estimator are examined. The proposed estimator is compared with previously known estimator by Monte Carlo study.